grpo-rlvr-training
Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.
Profile →npx skills add wshobson/agents --skill grpo-rlvr-training --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# GRPO & RLVR Training This skill assumes `finetuning-method-selection` already routed here because the target behavior has a verifiable pass/fail signal — not demonstrations (`lora-qlora-recipes`) or preference pairs (`preference-optimization`). What follows is when RL is the right tool, the reference recipe, the mandatory reward-inspection gate, and how to pick a GRPO variant when the base recipe misbehaves. **Input:** a routing decision (RLVR via GRPO) plus a verifier (code executor, test suite, schema checker, or grader) for the target task. **Output format:** a validated GRPO config — the kwarg values in `references/grpo-memory.md` and the reward functions in `references/reward-functions.md`, not free-form advice — that `llm-finetuning-training-engineer` consumes directly. ## When RL Applies GRPO+RLVR only pays off when task success is **algorithmically checkable** — a unit test passes, a parser accepts the output, a tool call matches an expected schema, a math answer matches a ground truth. If grading the output requires human judgment or a subjective rubric, that's an eval-harness and judge-calibration problem first — see `eval-harness-first` — not a reason to skip straight
- When RL Applies
- The Recipe
- The Inspection Rule
- Variant Selection
- VLM RL Is Reference-Only
- References
What does the grpo-rlvr-training skill do?
Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.
How do I install it?
Run `npx skills add wshobson/agents --skill grpo-rlvr-training --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.